Isolation of Fetal ECG Signals from Abdominal ECG Using Wavelet Analysis

ObjectiveMonitoring the heartbeat of the fetus during pregnancy is a vital part in determining their health. Current fetal heart monitoring techniques lack the accuracy in fetal heart rate monitoring and features acquisition, resulting in diagnostic medical issues. The demand for a reliable method of non-invasive fetal heart monitoring is of high importance.MethodElectrocardiogram (ECG) is a method of monitoring the electrical activity produced by the heart. The extraction of the fetal ECG (FECG) from the abdominal ECG (AECG) is challenging since both ECGs of the mother and the baby share similar frequency components, adding to the fact that the signals are corrupted by white noise. This paper presents a method of FECG extraction by eliminating all other signals using AECG. The algorithm is based on attenuating the maternal ECG (MECG) by filtering and wavelet analysis to find the locations of the FECG, and thus isolating them based on their locations. Two signals of AECG collected at different locations on the abdomens are used. The ECG data used contains MECG of a power of five to ten times that of the FECG.ResultsThe FECG signals were successfully isolated from the AECG using the proposed method through which the QRS complex of the heartbeat was conserved, and heart rate was calculated. The fetal heart rate was 135 bpm and the instantaneous heart rate was 131.58 bpm. The heart rate of the mother was at 90 bpm with an instantaneous heart rate of 81.9 bpm.ConclusionThe proposed method is promising for FECG extraction since it relies on filtering and wavelet analysis of two abdominal signals for the algorithm. The method implemented is easily adjusted based on the power levels of signals, giving it great ease of adaptation to changing signals in different biosignals applications.     

Techniques of EMG signal analysis: detection, processing, classification and applications

Electromyography (EMG) signals can be used for clinical/biomedical applications, Evolvable Hardware Chip (EHW) development, and modern human computer interaction. EMG signals acquired from muscles require advanced methods for detection, decomposition, processing, and classification. The purpose of this paper is to illustrate the various methodologies and algorithms for EMG signal analysis to provide efficient and effective ways of understanding the signal and its nature. We further point up some of the hardware implementations using EMG focusing on applications related to prosthetic hand control, grasp recognition, and human computer interaction. A comparison study is also given to show performance of various EMG signal analysis methods. This paper provides researchers a good understanding of EMG signal and its analysis procedures. This knowledge will help them develop more powerful, flexible, and efficient applications.     

Fetal ECG extraction during labor using an adaptive maternal beat subtraction technique.

Fetal ECG (FECG) monitoring using abdominal maternal signals is a non-invasive technique that allows early detection of changes in fetal wellbeing. Several other signal components have stronger energy than the FECG, the most important being maternal ECG (MECG) and, especially during labor, uterine EMG. This study proposes a new method to subtract MECG after detecting and removing abdominal signal segments with high-amplitude variations due to uterine contractions. The method removes MECG from abdominal signals using an approximation of the current MECG segment based on a linear combination of previous MECG segments aligned on the R-peak. The coefficients of the linear model are computed so that the squared error of the approximation over the whole current segment is minimized. Abdominal signal segments strongly affected by uterine contractions are detected by applying median filtering. The methods proposed are tested on real abdominal data recorded during labor, with FECG recorded using scalp electrodes synchronously recorded for comparison.     

An estimate of fetal autonomic state by time-frequency analysis of fetal heart rate variability.

In this study we present a noninvasive method that enables the investigation of the fetal heart rate (FHR) fluctuations. The objective was to design a quantitative measurement to assess the fetal autonomic nervous system and to investigate its development as a function of the gestational age. Our Medical Physics group has developed a complex algorithm for online beat-to-beat detection of the fetal ECG (FECG), extracted from the maternal abdominal ECG signal. We used our previously acquired FECG data, which includes noninvasive recordings of 200 maternal abdominal ECG signals. From these, we chose 35 cases of healthy pregnancies that we divided into three groups according to gestational age: Group 1, 23 +/- 2 wk; Group 2, 32 +/- 1 wk; and Group 3, 39 +/- 1 wk. The FHR variability was analyzed by a time-frequency decomposition based on a continuous wavelet transform. We showed that, independent of the gestational age, most of the FHR power is concentrated in the very-low-frequency range (0.02-0.08 Hz) and in the low-frequency range (0.08-0.2 Hz). In addition, there is power in the high-frequency range that correlates with the frequency range of fetal respiratory motion (0.4-1.7 Hz). In the intermediate-frequency range (0.2-0.4 Hz), the power is significantly smaller. The changes in the average power spectrum in relation to gestation time were carefully and quantitatively examined. The results imply that there is a neural organization during the last trimester of the pregnancy, and the sympathovagal balance is reduced with the gestational age.     

A new method for the extraction of fetal ECG from the dependent abdominal signals using blind source separation and adaptive noise cancellation techniques

Background

The electrocardiogram (ECG) is a diagnostic tool that records the electrical activity of the heart, and depicts it as a series of graph-like tracings, or waves. Being able to interpret these details allows diagnosis of a wide range of heart problems. Fetal electrocardiogram (FECG) extraction has an important impact in medical diagnostics during the mother pregnancy period. Since the observed FECG signals are often mixed with the maternal ECG (MECG) and the noise induced by the movement of electrodes or by mother motion, the separation process of the ECG signal sources from the observed data becomes quite complicated. One of its complexity is when the ECG sources are dependent, thus, in this paper we introduce a new approach of blind source separation (BSS) in the noisy context for both independent and dependent ECG signal source. This approach consist in denoising the observed ECG signals using a bilateral total variation (BTV) filter; then minimizing the Kullbak-Leibler divergence between copula densities to separate the FECG signal from the MECG one.

Results

We present simulation results illustrating the performance of our proposed method. We will consider many examples of independent/dependent source component signals. The results will be compared with those of the classical method called independent component analysis (ICA) under the same conditions. The accuracy of source estimation is evaluated through a criterion, called again the signal-to-noise-ratio (SNR). The first experiment shows that our proposed method gives accurate estimation of sources in the standard case of independent components, with performance around 27 dB in term of SNR. In the second experiment, we show the capability of the proposed algorithm to successfully separate two noisy mixtures of dependent source components - with classical criterion devoted to the independent case - fails, and that our method is able to deal with the dependent case with good performance.

Conclusions

In this work, we focus specifically on the separation of the ECG signal sources taken from skin two electrodes located on a pregnant woman’s body. The ECG separation is interpreted as a noisy linear BSS problem with instantaneous mixtures. Firstly, a denoising step is required to reduce the noise due to motion artifacts using a BTV filter as a very effective one-pass filter for denoising. Then, we use the Kullbak-Leibler divergence between copula densities to separate the fetal heart rate from the mother one, for both independent and dependent cases.

     

Cognitive neuroscience of creativity: EEG based approaches

Cognitive neuroscience of creativity has been extensively studied using non-invasive electrical recordings from the scalp called electroencephalograms (EEGs) and event related potentials (ERPs). The paper discusses major aspects of performing research using EEG/ERP based experiments including the recording of the signals, removing noise, estimating ERP signals, and signal analysis for better understanding of the neural correlates of processes involved in creativity. Important factors to be kept in mind to record clean EEG signal in creativity research are discussed. The recorded EEG signal can be corrupted by various sources of noise and methodologies to handle the presence of unwanted artifacts and filtering noise are presented followed by methods to estimate ERPs from the EEG signals from multiple trials. The EEG and ERP signals are further analyzed using various techniques including spectral analysis, coherence analysis, and non-linear signal analysis. These analysis techniques provide a way to understand the spatial activations and temporal development of large scale electrical activity in the brain during creative tasks. The use of this methodology will further enhance our understanding the processes neural and cognitive processes involved in creativity.     

Amélioration de l’estimation du rythme cardiaque fœtal en utilisant les signaux Doppler directionnels et YIN

Techniques dedicated to the fetal heart rate detection identify the patterns that repeat themselves over the time. The heart rate estimation is algorithmically similar to the estimation of the fundamental frequency (pitch) of voice signals. The new YIN technique introduced for the estimation of the fundamental frequency is applied to fetal heart rate estimation from the directional Doppler signals. We compare the performances in terms of probability of detection and accuracy of the estimation of the technique YIN with those of the cross-correlation, implemented in the Oxford SONICAID? monitors. A better detection probability and accuracy of estimation of fetal heart rate was obtained in case of YIN.     

A Comparison of ECG Signal Pre-processing Using FrFT,FrWT and IPCA for Improved Analysis

ObjectiveElectrocardiogram (ECG) is a diagnostic tool for recording electrical activities of the human heart non-invasively. It is detected by electrodes placed on the surface of the skin in a conductive medium. In medical applications, ECG is used by cardiologists to observe heart anomalies (cardiovascular diseases) such as abnormal heart rhythms, heart attacks, effects of drug dosage on subject's heart and knowledge of previous heart attacks. Recorded ECG signal is generally corrupted by various types of noise/distortion such as cardiac (isoelectric interval, prolonged depolarization and atrial flutter) or extra cardiac (respiration, changes in electrode position, muscle contraction and power line noise). These factors hide the useful information and alter the signal characteristic due to low Signal-to-Noise Ratio (SNR). In such situations, any failure to judge the ECG signal correctly may result in a delay in the treatment and harm a subject (patient) health. Therefore, appropriate pre-processing technique is necessary to improve SNR to facilitate better treatment to the subject. Effects of different pre-processing techniques on ECG signal analysis (based on R-peaks detection) are compared using various Figures of Merit (FoM) such as sensitivity (Se), accuracy (Acc) and detection error rate (DER) along with SNR.MethodsIn this research article, a new fractional wavelet transform (FrWT) has been proposed as a pre-processing technique in order to overcome the disadvantages of other existing commonly used techniques viz. wavelet transform (WT) and the fractional Fourier transform (FrFT). The proposed FrWT technique possesses the properties of multiresolution analysis and represents signal in the fractional domain which consists of representation in terms of rotation of signals in the time–frequency plane. In the literature, ECG signal analysis has been improvised using statistical pre-processing techniques such as principal component analysis (PCA), and independent component analysis (ICA). However, both PCA and ICA are prone to suffer from slight alterations in either signal or noise, unless the basis functions are prepared with a worldwide set of ECG. Independent Principal Component Analysis (IPCA) has been used to overcome this shortcoming of PCA and ICA. Therefore, in this paper three techniques viz. FrFT, FrWT and IPCA are selected for comparison in pre-processing of ECG signals.ResultsThe selected methods have been evaluated on the basis of SNR, Se, Acc and DER of the detected ECG beats. FrWT yields the best results among all the methods considered in this paper; 34.37dB output SNR, 99.98% Se, 99.96% Acc, and 0.036% DER. These results indicate the quality of biology-related information retained from the pre-processed ECG signals for identifying different heart abnormalities.ConclusionCorrect analysis of the acquired ECG signal is the main challenge for cardiologist due to involvement of various types of noises (high and low frequency). Twenty two real time ECG records have been evaluated based on various FoM such as SNR, Se, Acc and DER for the proposed FrWT and existing FrFT and IPCA preprocessing techniques. Acquired real-time ECG database in normal and disease situations is used for the purpose. The values of FoMs indicate high SNR and better detection of R-peaks in a ECG signal which is important for the diagnosis of cardiovascular disease. The proposed FrWT outperforms all other techniques and holds both analytical attributes of the actual ECG signal and alterations in the amplitudes of various ECG waveforms adequately. It also provides signal portrayals in the time-fractional-frequency plane with low computational complexity enabling their use practically for versatile applications.     

A Simple and Effective Approach for Digitization of the CTG Signals from CTG Traces

ObjectivesCardiotocography (CTG) is a useful tool for monitoring of the fetal heart rate (FHR) and uterine contractions (UC) during the intrauterine life. Generally, CTG is provided on a printed paper which is hard to save for future evaluations. So, digitization of CTG signals is in demand for future evaluations. A straightforward approach for digitization of the CTG signals is to apply image processing on the scanned CTG printed papers.Material and methodsIn this paper, an automatic procedure is proposed for digitization of the CTG signals. The proposed approach consists of four main stages such as pre-processing, image segmentation, signal extraction and signal calibration. The pre-processing stage covers median filtering and contrasts limited adaptive histogram equalization (CLAHE) for noise removal and contrast enhancement. Image segmentation is used to binarize the CTG images for signal determination using the Otsu's thresholding algorithm. The signal extraction is carried out by a two-stepped algorithm. The acquired CTG signals are then calibrated for obtaining the final CTG signals. We use the correlation coefficient to measure the similarity between the automatically digitized CTG signals and original signals.ResultsIn experimental works, an open-access database, which contains 552 CTG recordings, is employed. The results are quite impressive. According to the obtained results, the average correlation coefficients for FHR and UC signals are 0.9715 ± 0.0168 and 0.9717 ± 0.0465, respectively.ConclusionsThe obtained results show that the proposed method is quite efficient in digitization of the CTG signals. In future works, this tool will be used to digitize the recordings belonging to the antepartum period collected from the obstetrics clinics in Medical Park Hospital in Elazığ, Turkey.     

An integrated diabetic index using heart rate variability signal features for diagnosis of diabetes

Electrocardiogram (ECG) signals are difficult to interpret, and clinicians must undertake a long training process to learn to diagnose diabetes from subtle abnormalities in these signals. To facilitate these diagnoses, we have developed a technique based on the heart rate variability signal obtained from ECG signals. This technique uses digital signal processing methods and, therefore, automates the detection of diabetes from ECG signals. In this paper, we describe the signal processing techniques that extract features from heart rate (HR) signals and present an analysis procedure that uses these features to diagnose diabetes. Through statistical analysis, we have identified the correlation dimension, Poincaré geometry properties (SD2), and recurrence plot properties (REC, DET, L _mean) as useful features. These features differentiate the HR data of diabetic patients from those of patients who do not have the illness, and have been validated by using the AdaBoost classifier with the perceptron weak learner (yielding a classification accuracy of 86%). We then developed a novel diabetic integrated index (DII) that is a combination of these nonlinear features. The DII indicates whether a particular HR signal was taken from a person with diabetes. This index aids the automatic detection of diabetes, thereby allowing a more objective assessment and freeing medical professionals for other tasks.     

An algorithm for FHR estimation from foetal phonocardiographic signals

The long-term foetal surveillance is often to be recommended. Hence, the fully non-invasive acoustic recording, through maternal abdomen, represents a valuable alternative to the ultrasonic cardiotocography. Unfortunately, the recorded heart sound signal is heavily loaded by noise, thus the determination of the foetal heart rate raises serious signal processing issues. In this paper, we present a new algorithm for foetal heart rate estimation from foetal phonocardiographic recordings. A filtering is employed as a first step of the algorithm to reduce the background noise. A block for first heart sounds enhancing is then used to further reduce other components of foetal heart sound signals. A complex logic block, guided by a number of rules concerning foetal heart beat regularity, is proposed as a successive block, for the detection of most probable first heart sounds from several candidates. A final block is used for exact first heart sound timing and in turn foetal heart rate estimation. Filtering and enhancing blocks are actually implemented by means of different techniques, so that different processing paths are proposed. Furthermore, a reliability index is introduced to quantify the consistency of the estimated foetal heart rate and, based on statistic parameters; [,] a software quality index is designed to indicate the most reliable analysis procedure (that is, combining the best processing path and the most accurate time mark of the first heart sound, provides the lowest estimation errors). The algorithm performances have been tested on phonocardiographic signals recorded in a local gynaecology private practice from a sample group of about 50 pregnant women. Phonocardiographic signals have been recorded simultaneously to ultrasonic cardiotocographic signals in order to compare the two foetal heart rate series (the one estimated by our algorithm and the other provided by cardiotocographic device). Our results show that the proposed algorithm, in particular some analysis procedures, provides reliable foetal heart rate signals, very close to the reference cardiotocographic recordings.     

Is fetal heart rate monitoring sufficiently sensitive to detect changes during labour?

Although fetal monitoring is a common clinical procedure, there is little quantitative evidence that it can detect changes occurring during labour. We present quantitative data comparing the first and second stage of labour, from 21 labours resulting in a normal fetal outcome. A range of fetal heart rate variables was calculated from the output of a fetal heart rate monitor. Significant changes were detected in baseline fetal heart rate (P < 0.005), heart rate variability (P < 0.05), number of dips (P < 0.01).and their depth (P < 0.01). The results encourage confidence in the sensitivity of fetal monitoring for the detection of changes in a number of fetal heart rate variables during the course of labour.     

细菌群体感应信号分子的光电检测技术

群体感应（quorum sensing，QS）是一种依赖菌群密度的细菌交流系统。在探究细菌群体感应系统的调控机制中，对QS信号分子的鉴别和检测是不可或缺的环节，其对生命科学、药学等领域涉及细菌等微生物的相互作用、高效检测和作用机制解析等具有重要的参考意义。本文在总结不同类型细菌QS信号分子来源和结构的基础上，对QS信号分子的光电检测方法和技术进行了综述，重点对光电传感检测的敏感介质、传感界面、传感机制及测试效果进行探讨，同时关注了将微流控芯片分析技术应用于细菌QS信号分子原位监测的相关研究进展。     

The physiologic and medical rationale for intrapartum fetal monitoring

In the United States and most industrialized countries, intrapartum fetal surveillance is performed primarily by electronic fetal heart rate monitoring. Following implementation of this technology into clinical practice, a reduction in perinatal mortality has been accompanied by a concomitant increase in the cesarean section rate to concerning levels. Although these trends are not solely due to one factor such as electronic fetal heart rate monitoring, it is well-recognized that this method of surveillance is hampered by subjectivity in interpretation and by a high false-positive (falsely nonreassuring) rate. The purpose of this review is to assess the physiologic rationale for intrapartum assessment, the significant limitations of current primary and ancillary monitoring methods, and the development of new technologies such as fetal oxygen saturation monitoring (pulse oximetry) that potentially hold great promise for the future.     

Segmentation of heart sounds based on dynamic clustering

The heart sound signal is first separated into cycles, where the cycle detection is based on an instantaneous cycle frequency. The heart sound data of one cardiac cycle can be decomposed into a number of atoms characterized by timing delay, frequency, amplitude, time width and phase. To segment heart sounds, we made a hypothesis that the atoms of a heart sound congregate as a cluster in time–frequency domains. We propose an atom density function to indicate clusters. To suppress clusters of murmurs and noise, weighted density function by atom energy is further proposed to improve the segmentation of heart sounds. Therefore, heart sounds are indicated by the hybrid analysis of clustering and medical knowledge. The segmentation scheme is automatic and no reference signal is needed. Twenty-six subjects, including 3 normal and 23 abnormal subjects, were tested for heart sound signals in various clinical cases. Our statistics show that the segmentation was successful for signals collected from normal subjects and patients with moderate murmurs.     

A Wearable System for In-Home and Long-Term Assessment of Fetal Movement

Objectives: This paper presents a novel wearable system for in-home and long-term fetal movement monitoring on a reliable and easily accessible basis.Material and methods: The system mainly consists of four accelerometers for fetal movement signal acquisition, a microcontroller for signal processing and an Android-based device interacting with the microcontroller via Bluetooth Low Energy (BLE), providing the mother with information related to the fetal movement in an intelligible way.Results: The proposed system can deliver reliable results with a specificity of 0.99 and a sensitivity of 0.77 for fetal movement time series signal classification.Conclusion: The proposed wearable system will provide a good alternative to optimize the use of medical professionals and hospital resources, and has potential applications in the field of e-Health home care. Besides, the fetal movement acceleration signals acquired with volunteers (pregnant women) help establish an initial database for future medical analysis of sensor-recorded fetal behaviors.     

A modified Zeeman model for producing HRV signals and its application to ECG signal generation

Developing a mathematical model for the artificial generation of electrocardiogram (ECG) signals is a subject that has been widely investigated. One of the challenges is to generate ECG signals with a wide range of waveforms, power spectra and variations in heart rate variability (HRV)--all of which are important indexes of human heart functions. In this paper we present a comprehensive model for generating such artificial ECG signals. We incorporate into our model the effects of respiratory sinus arrhythmia, Mayer waves and the important very low-frequency component in the power spectrum of HRV. We use a new modified Zeeman model for generating the time series for HRV, and a single cycle of ECG is produced by using a simple neural network. The importance of the work is the model's ability to produce artificial ECG signals that resemble experimental recordings under various physiological conditions. As such the model provides a useful tool to simulate and analyse the main characteristics of ECG, such as its power spectrum and HRV under different conditions. Potential applications of this model include using the generated ECG as a flexible signal source to assess the effectiveness of a diagnostic ECG signal-processing device.     

A new technique for the identification of respiratory sinus arrhythmia in utero

Fetal heart rate (FHR) monitoring forms the basis of routine fetal assessment, particularly short-term variability in the interbeat interval which can be difficult to interpret. Respiratory sinus arrhythmia (RSA), the change in heart rate in response to breathing, contributes to short-term variability, and the presence of RSA in utero may reflect the functional integrity of the central nervous system. This paper describes the use of Doppler ultrasound to derive the required measures of fetal heart rate and fetal breathing movements and spectral analysis to identify RSA. Cases are presented to illustrate the results obtained both in the presence and absence of RSA.